OpenRaiser/Intern-Atlas huggingface dataset:
Fixed-Mobile Convergence in the 5G era: From Hybrid Access to Converged Core
Venue: arXiv cs.NI 2018
Year: 2018

The availability of different paths to communicate to a user or device introduces several benefits, from boosting enduser performance to improving network utilization. Hybrid access is a first step in enabling convergence of mobile and fixed networks, however, despite traffic optimization, this approach is limited as fixed and mobile are still two separate core networks inter-connected through an aggregation point. On the road to 5G networks, the design trend is moving towards an aggregated network, where different access technologies share a common anchor point in the core. This enables further network optimization in addition to hybrid access, examples are userspecific policies for aggregation and improved traffic balancing across different accesses according to user, network, and service context. This paper aims to discuss the ongoing work around hybrid access and network convergence by Broadband Forum and 3GPP. We present some testbed results on hybrid access and analyze some primary performance indicators such as achievable data rates, link utilization for aggregated traffic and session setup latency. We finally discuss the future directions for network convergence to enable future scenarios with enhanced configuration capabilities for fixed and mobile convergence.
Energy-Efficient Cellular Communications Powered by Smart Grid Technology
Venue: arXiv cs.NI 2018
Year: 2018

The energy efficiency aspect of cellular networks is a vital topic of research over the recent days. As energy consumption is on the rise and the price of electricity is increasing very rapidly, necessity to reduce electricity usage in every aspect is becoming much more significant. The power grid infrastructure, from which the cellular networks attain the required electricity for operation is considering a significant change from the traditional electricity grid to the smart grid. The base stations, which are the main candidates for energy consumption in cellular networks, remain in operation even when there are a very few users or no user at all. In this paper, we propose an effective application scenario of the femtocell technology for power saving purpose of cellular networks. Effective deployment of femto-access-points in the required places can reduce the power usage of cellular networks by turning off the redundant base stations. Moreover, the signal to noise plus interference ratio is also enhanced in comparison to the traditional scheme. The base station turn off probability and the energy consumption in the overall network are analyzed. The comparison results show that the proposed energy efficient model can significantly reduce energy consumption and increase the service quality of the cellular users.
A game theoretical approach for QoS provisioning in heterogeneous networks
Venue: arXiv cs.NI 2018
Year: 2018

With the proliferation of mobile phone users, interference management is a big concern in this neoteric years. To cope with this problem along with ensuring better Quality of Service (QoS), femtocell plays an imperious preamble in heterogeneous networks (HetNets) for some of its noteworthy characteristics. In this paper, we propose a game theoretic algorithm along with dynamic channel allocation and hybrid access mechanism with self-organizing power control scheme. With a view to resolving prioritized access issue, the concept of primary and secondary users is applied. Existence of pure strategy Nash equilibrium (NE) has been investigated and come to a perfection that our proposed scheme can be adopted both increasing capacity and increasing revenue of operators considering optimal price for consumers.
A New Vehicle Localization Scheme Based on Combined Optical Camera Communication and Photogrammetry
Venue: arXiv cs.NI 2018
Year: 2018

The demand for autonomous vehicles is increasing gradually owing to their enormous potential benefits. However, several challenges, such as vehicle localization, are involved in the development of autonomous vehicles. A simple and secure algorithm for vehicle positioning is proposed herein without massively modifying the existing transportation infrastructure. For vehicle localization, vehicles on the road are classified into two categories: host vehicles (HVs) are the ones used to estimate other vehicles' positions and forwarding vehicles (FVs) are the ones that move in front of the HVs. The FV transmits modulated data from the tail (or back) light, and the camera of the HV receives that signal using optical camera communication (OCC). In addition, the streetlight (SL) data are considered to ensure the position accuracy of the HV. Determining the HV position minimizes the relative position variation between the HV and FV. Using photogrammetry, the distance between FV or SL and the camera of the HV is calculated by measuring the occupied image area on the image sensor. Comparing the change in distance between HV and SLs with the change in distance between HV and FV, the positions of FVs are determined. The performance of the proposed technique is analyzed, and the results indicate a significant improvement in performance. The experimental distance measurement validated the feasibility of the proposed scheme.
A Comparative Survey of Optical Wireless Technologies: Architectures and Applications
Venue: arXiv cs.NI 2018
Year: 2018

New high-data-rate multimedia services and applications are evolving continuously and exponentially increasing the demand for wireless capacity of fifth-generation (5G) and beyond. The existing radio frequency (RF) communication spectrum is insufficient to meet the demands of future high-datarate 5G services. Optical wireless communication (OWC), which uses an ultra-wide range of unregulated spectrum, has emerged as a promising solution to overcome the RF spectrum crisis. It has attracted growing research interest worldwide in the last decade for indoor and outdoor applications. OWC offloads huge data traffic applications from RF networks. A 100 Gb/s data rate has already been demonstrated through OWC. It offers services indoors as well as outdoors, and communication distances range from several nm to more than 10000 km. This paper provides a technology overview and a review on optical wireless technologies, such as visible light communication, light fidelity, optical camera communication, free space optical communication, and light detection and ranging. We survey the key technologies for understanding OWC and present state-of-the-art criteria in aspects, such as classification, spectrum use, architecture, and applications. The key contribution of this paper is to clarify the differences among different promising optical wireless technologies and between these technologies and their corresponding similar existing RF technologies
On a Theorem of Kyureghyan and Pott
Venue: arXiv cs.CR 2018
Year: 2018

In the paper of Gohar M. Kyureghyan and Alexander Pott (Designs, Codes and Cryptography, 29, 149-164, 2003), the linear feedback polynomials of the Sidel'nikov-Lempel-Cohn-Eastman sequences were determined for some special cases. When referring to that paper, we found that Corollary 4 and Theorem 2 of that paper are wrong because there exist many counterexamples for these two results. In this note, we give some counterexamples of Corollary 4 and Theorem 2 of that paper.
Accelerated Labeling of Discrete Abstractions for Autonomous Driving Subject to LTL Specifications
Venue: arXiv cs.AI 2018
Year: 2018

Linear temporal logic and automaton-based run-time verification provide a powerful framework for designing task and motion planning algorithms for autonomous agents. The drawback to this approach is the computational cost of operating on high resolution discrete abstractions of continuous dynamical systems. In particular, the computational bottleneck that arises is converting perceived environment variables into a labeling function on the states of a Kripke structure or analogously the transitions of a labeled transition system. This paper presents the design and empirical evaluation of an approach to constructing the labeling function that exposes a large degree of parallelism in the operation as well as efficient memory access patterns. The approach is implemented on a commodity GPU and empirical results demonstrate the efficacy of the labeling technique for real-time planning and decision-making.
Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

This paper demonstrates that word sense disambiguation (WSD) can improve neural machine translation (NMT) by widening the source context considered when modeling the senses of potentially ambiguous words. We first introduce three adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant processes, and random walks, which are then applied to large word contexts represented in a low-rank space and evaluated on SemEval shared-task data. We then learn word vectors jointly with sense vectors defined by our best WSD method, within a state-of-the-art NMT system. We show that the concatenation of these vectors, and the use of a sense selection mechanism based on the weighted average of sense vectors, outperforms several baselines including sense-aware ones. This is demonstrated by translation on five language pairs. The improvements are above one BLEU point over strong NMT baselines, +4% accuracy over all ambiguous nouns and verbs, or +20% when scored manually over several challenging words.
Simulating acculturation dynamics between migrants and locals in relation to network formation
Venue: arXiv cs.MA 2018
Year: 2018

International migration implies the coexistence of different ethnic and cultural groups in the receiving country. The refugee crisis of 2015 has resulted in critical levels of opinion polarization on the question of whether to welcome migrants, causing clashes in receiving countries. This scenario emphasizes the need to better understand the dynamics of mutual adaptation between locals and migrants, and the conditions that favor successful integration. Agent-based simulations can help achieve this goal. In this work, we introduce our model MigrAgent and our preliminary results. The model synthesizes the dynamics of migration intake and post-migration adaptation. It explores the different acculturation outcomes that can emerge from the mutual adaptation of a migrant population and a local population depending on their degree of tolerance. With parameter sweeping, we detect how different acculturation strategies can coexist in a society and in different degrees among various subgroups. The results show higher polarization effects between a local population and a migrant population for fast intake conditions. When migrant intake is slow, transitory conditions between acculturation outcomes emerge for subgroups, e.g., from assimilation to integration for liberal migrants and from marginalization to separation for conservative migrants. Relative group sizes due to speed of intake cause counterintuitive scenarios, such as the separation of liberal locals. We qualitatively compare the processes of our model with the German portion sample of the survey Causes and Consequences of Socio-Cultural Integration Processes among New Immigrants in Europe (SCIP), finding preliminary confirmation of our assumptions and results.
FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network
Venue: arXiv cs.RO 2018
Year: 2018

Tactile sensing is essential to the human perception system, so as to robot. In this paper, we develop a novel optical-based tactile sensor "FingerVision" with effective signal processing algorithms. This sensor is composed of soft skin with embedded marker array bonded to rigid frame, and a web camera with a fisheye lens. While being excited with contact force, the camera tracks the movements of markers and deformation field is obtained. Compared to existing tactile sensors, our sensor features compact footprint, high resolution, and ease of fabrication. Besides, utilizing the deformation field estimation, we propose a slip classification framework based on convolution Long Short Term Memory (convolutional LSTM) networks. The data collection process takes advantage of the human sense of slip, during which human hand holds 12 daily objects, interacts with sensor skin and labels data with a slip or non-slip identity based on human feeling of slip. Our slip classification framework performs high accuracy of 97.62% on the test dataset. It is expected to be capable of enhancing the stability of robot grasping significantly, leading to better contact force control, finer object interaction and more active sensing manipulation.
Wikistat 2.0: Educational Resources for Artificial Intelligence
Venue: arXiv cs.CY 2018
Year: 2018

Big data, data science, deep learning, artificial intelligence are the key words of intense hype related with a job market in full evolution, that impose to adapt the contents of our university professional trainings. Which artificial intelligence is mostly concerned by the job offers? Which methodologies and technologies should be favored in the training programs? Which objectives, tools and educational resources do we needed to put in place to meet these pressing needs? We answer these questions in describing the contents and operational resources in the Data Science orientation of the specialty Applied Mathematics at INSA Toulouse. We focus on basic mathematics training (Optimization, Probability, Statistics), associated with the practical implementation of the most performing statistical learning algorithms, with the most appropriate technologies and on real examples. Considering the huge volatility of the technologies, it is imperative to train students in seft-training, this will be their technological watch tool when they will be in professional activity. This explains the structuring of the educational site github.com/wikistat into a set of tutorials. Finally, to motivate the thorough practice of these tutorials, a serious game is organized each year in the form of a prediction contest between students of Master degrees in Applied Mathematics for IA.
From direct tagging to Tagging with sentences compression
Venue: arXiv cs.CL 2018
Year: 2018

In essence, the two tagging methods (direct tagging and tagging with sentences compression) are to tag the information we need by using regular expression which basing on the inherent language patterns of the natural language. Though it has many advantages in extracting regular data, Direct tagging is not applicable to some situations. if the data we need extract is not regular and its surrounding words are regular is relatively regular, then we can use information compression to cut the information we do not need before we tagging the data we need. In this way we can increase the precision of the data while not undermine the recall of the data.
Managing Smartphones Signalling Load in UMTS Networks: A Practical Analysis
Venue: arXiv cs.NI 2018
Year: 2018

Smartphone users and application behaviors add high pressure to apply smart techniques that stabilize the network capacity and consequently improve the end-user experience. The massive increase in smartphone penetration engenders signalling load, which exceeds the network capacity in terms of signaling. The signalling load leads to network congestion, degradation in the network KPIs. The classical way to tackle the signalling is by network expansion. However, this approach is not efficient in terms of capital expenditure (CAPEX) and also in terms of efficient utilization of the network resources. More specifically, the signaling domain becomes overloaded while the data domain are underutilized. In this paper, two UMTS air-interface features; Cell-PCH (paging channel) and enhanced fast dormancy (E-FD) are analyzed to mitigate the signalling load. Practical performance analysis is conducted based on results from commercial UMTS networks. The deployment of these features offers major improvement in network KPIs and significant relief in the signaling load. It is concluded that these two features in addition to several optimization techniques, discussed in this paper, provide solution to the smartphone signaling load and efficiently utilize the available network and spectrum resources while providing users with better always-on connectivity and improved battery consumption.
When logic lays down the law
Venue: arXiv cs.AI 2018
Year: 2018

We analyse so-called computable laws, i.e., laws that can be enforced by automatic procedures. These laws should be logically perfect and unambiguous, but sometimes they are not. We use a regulation on road transport to illustrate this issue, and show what some fragments of this regulation would look like if rewritten in the image of logic. We further propose desiderata to be fulfilled by computable laws, and provide a critical platform from which to assess existing laws and a guideline for composing future ones.
Optimal Sabotage Attack on Composite Material Parts
Venue: arXiv cs.CR 2018
Year: 2018

Industry 4.0 envisions a fully automated manufacturing environment, in which computerized manufacturing equipment--Cyber-Physical Systems (CPS)--performs all tasks. These machines are open to a variety of cyber and cyber-physical attacks, including sabotage. In the manufacturing context, sabotage attacks aim to damage equipment or degrade a manufactured part's mechanical properties. In this paper, we focus on the latter, specifically for composite materials. Composite material parts are predominantly used in safety-critical systems, e.g., as load-bearing parts of aircraft. Further, we distinguish between the methods to compromise various manufacturing equipment, and the malicious manipulations that will sabotage a part. As the research literature has numerous examples of the former, in this paper we assume that the equipment is already compromised, our discussion is solely on manipulations. We develop a simulation approach to designing sabotage attacks against composite material parts. The attack can be optimized by two criteria, minimizing the "footprint" of manipulations. We simulate two optimal attacks against the design of a spar, a load bearing component of an airplane wing. Our simulation identifies the minimal manipulations needed to degrade its strength to three desired levels, as well as the resulting failure characteristics. Last but not least, we outline an approach to identifying sabotaged parts.
Super-resolution radar imaging via convex optimization
Venue: arXiv cs.IT 2018
Year: 2018

A radar system emits probing signals and records the reflections. Estimating the relative angles, delays, and Doppler shifts from the received signals allows to determine the locations and velocities of objects. However, due to practical constraints, the probing signals have finite bandwidth B, the received signals are observed over a finite time interval of length T only, and a radar typically has only one or a few transmit and receive antennas. These constraints fundamentally limit the resolution up to which objects can be distinguished. Specifically, a radar can not distinguish objects with delay and Doppler shifts much closer than 1/B and 1/T, respectively, and a radar system with N_T transmit and N_R receive antennas cannot distinguish objects with angels closer than 1/(N_T N_R). As a consequence, the delay, Doppler, and angular resolution of standard radars is proportional to 1/B and 1/T, and 1/(N_T N_R). In this chapter, we show that the continuous angle-delay-Doppler triplets and the corresponding attenuation factors can be resolved at much finer resolution, using ideas from compressive sensing. Specifically, provided the angle-delay-Doppler triplets are separated either by factors proportional to 1/(N_T N_R-1) in angle, 1/B in delay, or 1/T in Doppler direction, they can be recovered a significantly smaller scale or higher resolution.
Text-based Sentiment Analysis and Music Emotion Recognition
Venue: arXiv cs.CL 2018
Year: 2018

Sentiment polarity of tweets, blog posts or product reviews has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. Deep learning techniques are becoming top performers on analyzing such texts. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First, deep neural networks need to be fed with data sets that are big in size as well as properly labeled. Second, there are various uncertainties regarding the use of word embedding vectors: should they be generated from the same data set that is used to train the model or it is better to source them from big and popular collections? Third, to simplify model creation it is convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song data sets are created utilizing social tags. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted, trying word embeddings of various parameters. Regarding the third problem, a series of experiments involving convolution and max-pooling neural layers were conducted. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews.
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning
Venue: arXiv cs.RO 2018
Year: 2018

Prediction is an appealing objective for self-supervised learning of behavioral skills, particularly for autonomous robots. However, effectively utilizing predictive models for control, especially with raw image inputs, poses a number of major challenges. How should the predictions be used? What happens when they are inaccurate? In this paper, we tackle these questions by proposing a method for learning robotic skills from raw image observations, using only autonomously collected experience. We show that even an imperfect model can complete complex tasks if it can continuously retry, but this requires the model to not lose track of the objective (e.g., the object of interest). To enable a robot to continuously retry a task, we devise a self-supervised algorithm for learning image registration, which can keep track of objects of interest for the duration of the trial. We demonstrate that this idea can be combined with a video-prediction based controller to enable complex behaviors to be learned from scratch using only raw visual inputs, including grasping, repositioning objects, and non-prehensile manipulation. Our real-world experiments demonstrate that a model trained with 160 robot hours of autonomously collected, unlabeled data is able to successfully perform complex manipulation tasks with a wide range of objects not seen during training.
Deep convolutional Gaussian processes
Venue: arXiv cs.LG 2018
Year: 2018

We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared to current Gaussian process approaches on the MNIST and CIFAR-10 datasets. In particular, we improve CIFAR-10 accuracy by over 10 percentage points.